Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	127,528
2	South Dakota	121,637
3	Rhode Island	106,676
4	Utah	106,265
5	Tennessee	102,793
6	Arizona	101,748
7	Wisconsin	100,576
8	Iowa	100,192
9	Nebraska	97,693
10	Oklahoma	95,808
11	Arkansas	95,753
12	Kansas	94,554
13	Indiana	92,135
14	Alabama	91,590
15	Idaho	90,298
16	Mississippi	90,275
17	Nevada	88,970
18	Wyoming	88,755
19	Illinois	87,974
20	Montana	86,658
21	Louisiana	84,412
22	South Carolina	82,978
23	California	82,321
24	New Mexico	81,574
25	Georgia	81,171
26	Minnesota	81,101
27	Kentucky	79,870
28	Texas	79,693
29	Missouri	79,393
30	Florida	78,041
31	Delaware	77,875
32	New Jersey	76,167
33	Ohio	75,137
34	Massachusetts	73,921
35	Alaska	72,889
36	New York	70,389
37	North Carolina	70,131
38	Connecticut	69,374
39	Colorado	68,148
40	West Virginia	65,717
41	Pennsylvania	64,381
42	Michigan	60,368
43	Maryland	57,352
44	Virginia	57,237
45	District of Columbia	50,818
46	New Hampshire	46,747
47	Washington	40,657
48	Puerto Rico	39,408
49	Oregon	33,208
50	Maine	28,395
51	Vermont	18,235
52	Hawaii	17,915

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Rhode Island	1,030
2	Connecticut	890
3	Arizona	825
4	Texas	699
5	Kansas	670
6	Virginia	628
7	Louisiana	613
8	Georgia	597
9	South Carolina	576
10	New York	571
11	Kentucky	570
12	California	566
13	Arkansas	541
14	Alabama	538
15	New Jersey	515
16	Oklahoma	507
17	Mississippi	498
18	North Carolina	475
19	West Virginia	459
20	Massachusetts	457
21	Utah	444
22	Delaware	430
23	Florida	414
24	Ohio	398
25	Pennsylvania	397
26	Maine	389
27	Tennessee	371
28	Idaho	333
29	Nevada	326
30	Iowa	314
31	Indiana	304
32	New Hampshire	300
33	Montana	298
34	New Mexico	292
35	Missouri	288
36	Maryland	279
37	Illinois	278
38	Nebraska	270
39	Wyoming	270
40	District of Columbia	266
41	Colorado	259
42	Michigan	251
43	Wisconsin	241
44	Washington	222
45	Vermont	184
46	South Dakota	173
47	Alaska	154
48	North Dakota	150
49	Oregon	149
50	Minnesota	137
51	Puerto Rico	126
52	Hawaii	64

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,390
2	New York	2,181
3	Massachusetts	2,075
4	Rhode Island	2,015
5	Mississippi	1,988
6	South Dakota	1,965
7	Connecticut	1,956
8	North Dakota	1,890
9	Louisiana	1,868
10	Arizona	1,740
11	Illinois	1,653
12	Pennsylvania	1,648
13	Arkansas	1,571
14	Michigan	1,532
15	New Mexico	1,526
16	Alabama	1,462
17	Indiana	1,462
18	Iowa	1,426
19	Tennessee	1,351
20	Nevada	1,343
21	South Carolina	1,296
22	Kansas	1,276
23	District of Columbia	1,268
24	Georgia	1,245
25	Texas	1,242
26	Florida	1,202
27	Missouri	1,159
28	Maryland	1,157
29	Montana	1,124
30	Delaware	1,100
31	Minnesota	1,097
32	West Virginia	1,089
33	Wisconsin	1,083
34	Nebraska	1,031
35	Wyoming	1,029
36	California	985
37	Colorado	980
38	Idaho	960
39	Ohio	935
40	Oklahoma	856
41	North Carolina	854
42	Kentucky	851
43	New Hampshire	739
44	Virginia	729
45	Washington	563
46	Puerto Rico	561
47	Utah	505
48	Oregon	459
49	Maine	418
50	Alaska	344
51	Hawaii	283
52	Vermont	275

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Alabama	34
2	Tennessee	22
3	Arizona	19
4	Mississippi	16
5	Rhode Island	16
6	Arkansas	15
7	California	15
8	Montana	15
9	Connecticut	14
10	Hawaii	14
11	Wyoming	14
12	Kansas	13
13	Georgia	12
14	Nevada	12
15	Pennsylvania	12
16	South Dakota	12
17	Texas	11
18	Delaware	10
19	District of Columbia	10
20	New Jersey	10
21	West Virginia	10
22	New York	9
23	Oklahoma	9
24	Florida	8
25	Idaho	8
26	Kentucky	8
27	Louisiana	8
28	Massachusetts	8
29	Missouri	8
30	New Mexico	8
31	South Carolina	8
32	Illinois	7
33	Maryland	7
34	Nebraska	7
35	Indiana	6
36	North Carolina	6
37	Ohio	6
38	Wisconsin	6
39	Virginia	5
40	Maine	4
41	Michigan	4
42	New Hampshire	4
43	Oregon	4
44	Washington	4
45	Colorado	3
46	North Dakota	2
47	Puerto Rico	2
48	Utah	2
49	Iowa	1
50	Minnesota	1
51	Vermont	1
52	Alaska	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	291,371	1	99
Chattahoochee	Georgia	235,720	2	99
Dewey	South Dakota	234,725	3	99
Lincoln	Arkansas	232,340	4	99
Bent	Colorado	230,411	5	99
Davidson	Tennessee	119,899	244	92
Richland	South Carolina	83,349	1394	55
York	South Carolina	76,433	1756	44
Orange	California	75,673	1788	43
Pierce	Washington	37,971	2891	7

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	8,346	1	99
Jerauld	South Dakota	7,948	2	99
Dickey	North Dakota	6,568	3	99
Gregory	South Dakota	6,452	4	99
Grant	Nebraska	6,421	5	99
Davidson	Tennessee	1,036	1881	40
Richland	South Carolina	962	2000	36
Orange	California	894	2097	33
York	South Carolina	765	2276	27
Pierce	Washington	504	2642	15

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.